Biomaterial Database

Neural modelling of the biodegradability of benzene derivatives. 1993

J Devillers

CTIS, Lyon, France.

The aim of this paper was to explore the usefulness of a backpropagation neural network (BNN) to estimate the biodegradability of benzene derivatives. 127 chemicals selected from the BIODEG data bank (Syracuse Research Corporation, 1992) were described by means of 20 structural descriptors taking into account the nature and position of the substituents on the benzene ring. Three classes of biodegradability were selected and modelled from the BNN. A 20/5/3 BNN (alpha = 0.8 and eta = 0.5) correctly classified 92% (104/113) of the training and 86% (12/14) of the testing sets. The results were compared to those produced by the BIODEG probability program (Syracuse Research Corporation, Version 2.13).

UI	MeSH Term	Description	Entries
D000465	Algorithms	A procedure consisting of a sequence of algebraic formulas and/or logical steps to calculate or determine a given task.	Algorithm
D001555	Benzene Derivatives	Organic compounds derived from BENZENE.	Derivatives, Benzene
D001673	Biodegradation, Environmental	Elimination of ENVIRONMENTAL POLLUTANTS; PESTICIDES and other waste using living organisms, usually involving intervention of environmental or sanitation engineers.	Bioremediation,Phytoremediation,Natural Attenuation, Pollution,Environmental Biodegradation,Pollution Natural Attenuation
D013329	Structure-Activity Relationship	The relationship between the chemical structure of a compound and its biological or pharmacological activity. Compounds are often classed together because they have structural characteristics in common including shape, size, stereochemical arrangement, and distribution of functional groups.	Relationship, Structure-Activity,Relationships, Structure-Activity,Structure Activity Relationship,Structure-Activity Relationships
D016571	Neural Networks, Computer	A computer architecture, implementable in either hardware or software, modeled after biological neural networks. Like the biological system in which the processing capability is a result of the interconnection strengths between arrays of nonlinear processing nodes, computerized neural networks, often called perceptrons or multilayer connectionist models, consist of neuron-like units. A homogeneous group of units makes up a layer. These networks are good at pattern recognition. They are adaptive, performing tasks by example, and thus are better for decision-making than are linear learning machines or cluster analysis. They do not require explicit programming.	Computational Neural Networks,Connectionist Models,Models, Neural Network,Neural Network Models,Neural Networks (Computer),Perceptrons,Computational Neural Network,Computer Neural Network,Computer Neural Networks,Connectionist Model,Model, Connectionist,Model, Neural Network,Models, Connectionist,Network Model, Neural,Network Models, Neural,Network, Computational Neural,Network, Computer Neural,Network, Neural (Computer),Networks, Computational Neural,Networks, Computer Neural,Networks, Neural (Computer),Neural Network (Computer),Neural Network Model,Neural Network, Computational,Neural Network, Computer,Neural Networks, Computational,Perceptron